Settlements in the presence of leniency programs: Costs and benefits
Bibliographic record
Abstract
Abstract Over the last few decades, leniency programs have become important components of anti‐cartel policies in many jurisdictions. An extensive literature shows how such programs can destabilize cartels and even discourage their formation in the first place. Much less studied are settlement policies under which reduced fines are offered to settling parties late in the prosecution (when the probability of conviction is high). In particular, there has been little attention paid to the interaction of leniency and settlement policies. This paper examines whether the availability of late‐stage settlements could negatively impact the effectiveness of early‐stage leniency programs. Our main finding is that an appropriately designed settlement program can make collusion more difficult: In equilibrium, the adoption of an optimal settlement program by the Antitrust Authority reduces the occurrence of cartels by decreasing the long‐run gain from collusion. However, an overly generous settlement policy may undermine leniency programs and encourage the formation of more cartels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".